English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we introduce a new machine learning (ML) model for nonlinearregression called the Boosted Smooth Transition Regression Trees (BooST), whichis a combination of boosting algorithms with smooth transition regressiontrees. The main advantage of the BooST model is the estimation of thederivatives (partial effects) of very general nonlinear models. Therefore, themodel can provide more interpretation about the mapping between the covariatesand the dependent variable than other tree-based models, such as RandomForests. We present several examples with both simulated and real data.

BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions